Search Results (172)

To address underground shotcrete support scenarios with potential radiation-protection requirements, a bismuth oxide/clay functional filler was incorporated into a baseline shotcrete formulation. Functional filler dosage, calcium formate dosage, and PCE dosage were selected as variables, and Box–Behnken response surface methodology was used to establish quadratic regression models for 28 d compressive strength, fluidity, and bond strength. Representative optimized mixtures were further evaluated by MCNP5 simulation, gamma-ray air-kerma attenuation tests, and SEM. The models showed good fitting and predictive performance within the investigated design space. Functional filler dosage mainly controlled compressive strength and bond strength, whereas PCE dosage dominated fluidity. Under the constraints of compressive strength ≥ 25 MPa, fluidity of 160–170 mm, and bond strength ≥ 0.8 MPa, three representative mixtures were selected for shielding-, strength-, and interface-priority strategies. Simulated and measured results showed consistent shielding-performance rankings, and the optimized mixtures exhibited higher gamma-ray attenuation than the blank mixture. BBD26 achieved the highest shielding performance, with measured shielding rates of 65.51% and 51.54% at 661.7 keV and 1.25 MeV, respectively. Thickness-gradient tests indicated exponential attenuation, while SEM revealed differences in Bi-bearing particle distribution and matrix continuity. Full article

An integrated rebar box connection is proposed for the horizontal joints of fully prefabricated composite walls to simplify joint detailing and reduce on-site wet construction. Experimental tests and numerical analyses were conducted to evaluate the behavior of this connection. The results show that both specimens exhibited shear-dominated failure. The box connection and horizontal joint did not experience obvious fracture or pull-out failure, although local cover spalling, mortar crushing, and connector deformation were observed, suggesting effective force transfer between the upper and lower wall panels under the tested conditions. Compared with the cyclically loaded specimen, the monotonically loaded specimen exhibited higher peak load and larger deformation capacity under monotonic loading, whereas the initial stiffness was similar. The numerical results agree reasonably well with the experimental responses. The parametric finite element analyses indicate that increasing the integrated rebar diameter, the longitudinal reinforcement ratio in the rib columns, the concrete grid strength, and the axial compression ratio improves the load-carrying capacity of the wall, although a higher axial compression ratio reduces ductility. The proposed connection shows promising potential for use in the horizontal joints of fully prefabricated composite walls, and further studies with additional specimens and comparative connection details are warranted. Full article

Accurate constitutive modeling of TC4 titanium alloy at elevated temperatures is critical for process design and numerical simulation in aerospace manufacturing. However, purely data-driven deep neural networks (DNNs) often suffer from severe overfitting and may yield physically unreasonable predictions in data-sparse or strictly out-of-distribution (OOD) regions. To address this issue, this study proposes a physics-guided two-stage neural network framework, termed NN-PhysicsInit, for the constitutive modeling of TC4 alloy. In Stage I, a large synthetic dataset generated from a strain-compensated Arrhenius-type constitutive equation is used to pre-train the network, thereby introducing analytical prior knowledge into the initial topological space. In Stage II, the pre-trained model is fine-tuned using rigorously corrected experimental data obtained from isothermal compression tests conducted over 800–980 °C and 0.001–1 s⁻¹ to improve material-specific predictive accuracy. To evaluate generalization capability, a rigorous dual-perspective extrapolation validation scheme is designed separately in the temperature (1010 °C) and strain-rate (10 s⁻¹) dimensions. The results demonstrate that, compared with direct black-box training, the proposed framework successfully prevents non-physical divergence and better preserves macroscopic thermodynamic smoothness in unseen domains. Specifically, the extrapolation average absolute relative error (AARE) is significantly reduced from 34.21% to 14.34% in the temperature extrapolation task, and from 27.91% to 8.92% in the strain-rate extrapolation task. These findings confirm that physics-based initialization acts as a powerful implicit regularizer, effectively mitigating the extrapolation catastrophe while maintaining high fitting accuracy. The proposed framework provides a robust and practical strategy for the constitutive modeling of complex alloys under limited-data conditions. Full article

The bond strength of corroded reinforced concrete (CRC) structures is critical for structural safety and long-term durability. However, the corrosion-induced bond degradation process is influenced by multiple, coupled factors and exhibits complex, nonlinear behavior, making it difficult for traditional theoretical models to provide accurate predictions. To address this challenge, this study proposes a novel, unified prediction framework based on machine learning techniques. A total of 391 experimental datasets were collected and compiled, covering key parameters including bond strength, reinforcing bar diameter, yield strength, concrete cover thickness, concrete compressive strength, mass loss rate due to corrosion, and the presence of stirrups. Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms were employed to develop predictive models for bond strength. Model training and testing were performed using 10-fold cross-validation. Furthermore, the SHapley Additive exPlanations (SHAP) approach was introduced to enhance model interpretability and quantitatively assess the influence of each input feature, revealing that mass loss rate and bar diameter are the dominant factors. This study effectively bridges the research gap between high-precision black-box algorithms and the need for physical interpretability in engineering. The results demonstrate that (1) the proposed XGBoost model significantly outperforms traditional empirical formulations, achieving a high coefficient of determination (R² = 0.893) and a much lower coefficient of variation (25.85%) on the testing set, and (2) the SHAP analysis reveals that the machine learning predictions are highly consistent with established physical mechanisms, successfully capturing the negative impact of splitting tensile stresses caused by rust expansion and the positive confinement effect of stirrups. Overall, the proposed models demonstrate superior accuracy, robustness, and generalization capability, providing an effective tool and theoretical basis for evaluating bond behavior and designing durable CRC structures with broad engineering applicability. Full article

Automated mobile microscopy in Internet of Things (IoT) networks is essential for scaling malaria screening in resource-constrained environments. Deploying standard convolutional architectures here introduces severe adversarial vulnerabilities. Post-Training Quantization (PTQ) mitigates hardware constraints by converting floating-point models to 8-bit integers (INT8); however, its impact on clinical safety and security remains unexplored. This study presents an adversarial audit of quantized Vision Transformers for medical edge deployment. We evaluated a Swin-Tiny transformer against ViT-Tiny and MobileNetV3 baselines using a 27,558-image malaria dataset and an out-of-distribution (OOD) White Blood Cell dataset. Our findings redefine the “Quantization Shield” hypothesis. PTQ compresses the Swin model by 3.9× (to 27.89 MB) with a negligible 0.11% accuracy drop, maintaining statistical reliability on OOD tests. However, the hypothesized architectural resilience shatters under white-box Projected Gradient Descent (PGD) attacks. Despite robustness against single-step attacks, both MobileNetV3 and the INT8 Swin-Tiny collapse to 0.00% accuracy under iterative PGD. Conversely, the quantized Swin-Tiny resists black-box transfer attacks from a surrogate, maintaining 81.00% accuracy. We conclude that while quantized Vision Transformers meet mobile sensor constraints, integer quantization provides zero innate defense against targeted iterative perturbations, exposing a critical vulnerability in diagnostic IoT networks. Full article

This paper introduced the Enhanced Puzzle Optimization Algorithm (EPOA), a hybrid metaheuristic that augmented the original Puzzle Optimization Algorithm (POA) with uniform crossover, random-resetting mutation, and explicit elitism. The contribution does not lie in inventing these operators individually, since they are classical search components, but in integrating them into POA’s two-phase search dynamics to address premature convergence, diversity loss, and best-solution preservation in a targeted manner. This paper formalized EPOA’s update rules, provided pseudocode and flow diagrams, and enforced bound handling for box-constrained problems. Comprehensive tests on the CEC2022 single-objective benchmark suite (F₁–F₁₂) showed that EPOA attained rank 1 on 11 of 12 functions and rank 3 on the remaining case, with large error reductions relative to baseline POA (e.g., on F₁, the mean error dropped from 62.836 to 0.004; on F₆, the mean error dropped from 2370.962 to 7.239). The method was further evaluated on six classical constrained engineering design problems (welded beam, tension/compression spring, speed reducer, pressure vessel, three-bar truss, and cantilever beam). Statistical indicators such as the mean and standard deviation were used to assess robustness. The results showed that EPOA delivered a strong exploration–exploitation balance and robust solution quality across rugged landscapes and real-world constraints. Full article

The prefabricated polyurethane-solidified track bed (PPSTB) combines the adjustability of ballasted tracks with the low maintenance requirements of slab tracks, offering a promising solution for railway sections on deformable foundations. This study investigates the interaction and mechanical behaviors of the polyurethane-solidified ballast (PSB) modules and bulk ballast under laboratory loading. A series of unconfined uniaxial tests, confined ballast box tests, and cyclic loading tests were conducted, complemented by discrete element method (DEM) simulations to analyze mesoscopic particle evolution. Under monotonic compression, the stress–strain curve exhibits three distinct stages with an average elastic modulus of 19.66 MPa, where the central aggregate framework acts as the primary load-bearing structure. Confinement increases the modulus by 33.57% and yields a nearly linear stress–strain relationship, attributed to a more compact and uniform contact distribution. Furthermore, under cyclic loading, the PSB shows enhanced energy dissipation and deformation resistance compared to conventional ballast. These findings provide a theoretical basis for the structural design and long-term performance assessment of the PPSTB. Full article

Moderate thermal exposure can significantly influence the mechanical behavior of volcanic rocks by inducing microcrack development and altering crack network characteristics. However, quantifying such damage processes remains challenging when relying solely on conventional mechanical parameters. In this study, the evolution of crack network complexity in andesite and andesitic–basaltic rocks subjected to moderate thermal exposure (200 °C) is investigated using fractal analysis integrated with mechanical and mineralogical observations. Six core specimens were tested under uniaxial compression, including three natural specimens and three specimens thermally treated at 200 °C prior to loading. After failure, crack surfaces were digitized and fractal dimensions (D) were calculated using the box-counting method. Petrographic observations and X-ray powder diffraction (XRPD) analyses were conducted to characterize the mineralogical composition and microstructural features controlling crack development. The results indicate that thermal exposure primarily reduces rock stiffness rather than peak strength. While the uniaxial compressive strength (UCS) of two specimens remains nearly unchanged after heating, the elastic modulus (E) decreases in all thermally treated specimens. Mineralogical observations reveal a heterogeneous volcanic fabric dominated by plagioclase and pyroxene within a fine-grained groundmass, with secondary calcite phases occurring in veins and pocket fillings. Fractal analysis shows generally lower D values in thermally treated specimens, suggesting crack redistribution and coalescence rather than increased network complexity, consistent with the observed reduction in stiffness and a tendency toward more ductile deformation behavior. Full article

In order to study the stability of orthotropic steel box girders and the characteristics of the synergistic stress mechanism of key components, the test method of axial compression using the scale model of steel box girder segments was carried out, and the collaborative working performance of the plate ribs of the U-shaped stiffener plate and the influence mechanism of the diaphragm on the structural stability were systematically studied. The results show that the strain difference between the deckplate and the U rib increases significantly with the increase in load, and the distribution law of the end chamber is larger than the middle, and the bottom plate is larger than the top plate and the web plate. The diaphragm mainly bears the tensile force under axial load, which provides out-of-plane restraint for the stiffener, and its restraint effect is the strongest at the web plate and the weakest at the bottom plate. This paper clarifies the synergistic stress mechanism of U-rib stiffeners under high axial pressure conditions, quantifies the contribution of diaphragms to local stability, and provides a theoretical basis for the structural design of similar bridges. Full article

Early-age cracking is a common issue in the prefabrication of large-scale box girders, and the application of pre-tensioning techniques to introduce pre-compressive stress is an effective measure to mitigate such cracking. To determine an optimal pre-tensioning scheme for the 60 m large-scale box girder used in the Ningbo–Xiangshan intercity railway, friction coefficient tests and field stress monitoring were conducted. A numerical model simulating the pre-tensioning process of the box girder, accounting for the constraint of the steel formwork, was developed using Abaqus 2021. Based on the validated finite element model, a parametric study was performed to investigate the effects of friction coefficient, internal formwork roof, and prestressing tendon arrangement on the pre-compressive stress. The results indicate that the bond force between cast-in-place concrete and steel formwork is approximately 2.1 times the sliding friction force. As the friction coefficient increases, the pre-compressive stress in the box girder exhibits a notable decreasing trend. For the critical midspan section S40, the inclusion of frictional effects results in a more uniform distribution of pre-compressive stress. Compared to the case without the internal formwork roof, its inclusion leads to a 9.2% to 10.4% reduction in pre-compressive stress at section S40. To mitigate prestress losses transmitted from the ends to the midspan section, it is recommended that the internal formwork be completely removed prior to prestressing tensioning. The pre-compressive stress in the box girder varies considerably with different prestressing combinations. The comparative analysis of different prestressing combinations reveals substantial variations in pre-compressive stress distribution. After evaluating multiple schemes, the optimal pre-tensioning sequence for the 60-m railway box girder is determined as follows: sequentially tensioning tendon groups F1-2, F1-4, F1-5, F1-6, and B2-3, with an anchorage stress controlled at 558 MPa. This scheme ensures that all critical sections of the box girder remain in a pre-compressive state. In particular, the pre-compressive stress at the key midspan section S40 ranges from 1.12 to 1.26 MPa, achieving the desired effect and effectively suppressing early-age cracking in the large-scale box girder concrete. Full article

Heterogeneous multi-core systems (HMCSs) typically face a dilemma: heuristics (e.g., Linux CFS) are fast but blind to global constraints, while meta-heuristics (e.g., GAs) are globally optimal but too slow for real-time OS interaction. To bridge this gap without relying on “black-box” neural networks, we introduce the Tissue P System-Inspired Task Allocator (TPSTA). By mapping HMCS and parallel task scheduling to Tissue P System models and vectorized linear algebra problems, TPSTA achieves a computational complexity of $\mathrm{O}\left(\mathrm{M}/\mathrm{W}\right)$, effectively compressing the decision space. Our rigorous evaluation across four dimensions reveals a system strictly bound by physical constraints rather than algorithmic heuristics. (1) Under sufficient resource provisioning (four chips), TPSTA achieves a 0.00% Deadline Miss Ratio (DMR). Crucially, stress tests on constrained hardware (two chips) show graceful degradation to a 12.88% DMR, matching the optimal theoretical bound of EDF, whereas standard heuristics collapse to failure rates > 68%. On a massive 4096-core cluster, TPSTA outperforms the Linux GTS scalar baseline by 14.4×, maintaining low latency where traditional algorithms fail (>8 s). (3) Adaptability: The system demonstrates adaptive routing in handling hardware heterogeneity; without explicit rule-coding, it autonomously prioritizes data locality during NUMA transfers and migrates compute-bound tasks during thermal throttling events. (4) Physical Limits: Finally, our roofline analysis confirms that while the algorithmic speedup is theoretically linear, practical performance saturates at ~375× due to the Memory Wall, validating the isomorphism between synaptic bandwidth and hardware memory channels. Full article

Mode II fracture toughness is an important material parameter of rocks, but accurate measurement of this parameter is still a challenge in rock fracture mechanics. This study aims to modify the mode II fracture toughness of red sandstone measured through shear box testing by emphasizing the critical role of crack initiation angle. Experimental tests combining fracture trajectory scanning and digital image correlation reveal distinct fracture mechanisms of red sandstone under varying loading angles: tensile spalling dominates low angles, and shear fractures emerge at medium angles, while tensile fracture initiates from the rock bridge center at high angles. Although shear fracture initiates from the notch tip, its initiation angle deviates from the initial crack plane, invalidating traditional mode II fracture toughness determination methods. A modified Mohr–Coulomb criterion incorporating fracture angle and Mode I stress intensity factor is proposed to correct the significant errors of traditional methods, and this study establishes a refined framework for mode II fracture toughness determination under compression–shear conditions. Full article

Bridges are critical components of transportation networks, and fire accidents can significantly impair their structural integrity, leading to safety risks and major economic losses. This study presents a comprehensive inspection, materials testing, repair, and field load testing program for a full-scale concrete box girder bridge (Delta Bridge, Alexandria, Egypt) following a fire exposure on two spans. A total of 28 concrete core samples were extracted and tested, revealing average compressive strengths of 48.50 MPa (slab), 53.90 MPa (web), and 45.88 MPa (columns), representing moderate reductions of approximately 8.5%, 7.9%, and 10.8%, respectively, relative to the original in situ concrete strength recorded during construction, and 29.2%, 43.7%, and 30.0% increases over the minimum acceptance limits specified by Egyptian code of practice (ECP 203). Tensile strength tests on reinforcement bars indicated an average yield strength reduction coefficient of 0.87, corresponding to an estimated peak exposure temperature of 600 °C, yet still satisfying Egyptian code requirements (≥500 MPa). Field static load tests using 40-ton tri-axle trucks demonstrated maximum midspan deflections of 6.7 mm in fire-exposed spans and full recovery (>94%) upon unloading, confirming that the residual stiffness and load-carrying capacity were within acceptable limits. Based on these results, a targeted repair program was executed, including concrete cover replacement with shotcrete; steel derusting; surface coating; and bearing replacement, followed by a verification load test that confirmed the effectiveness of the rehabilitation. This case study demonstrates a robust framework for post-fire condition assessment, residual capacity evaluation, and repair validation of concrete box girder bridges. The methodology and findings provide valuable guidance for engineers and transportation authorities in mitigating fire-induced risks and ensuring the safe reopening of critical bridge infrastructure. Full article

Grain breakage occurs in sand specimens subjected to high stress levels; however, the magnitude and characteristics of the resulting deformation remain insufficiently quantified. This study investigates particle-scale fracture behavior in a standardized quartz sand subjected to controlled mechanical loading. Rapid, unconsolidated–undrained (UU) direct shear box tests were performed under normal stresses of 700, 800, and 900 kPa to induce grain breakage. The mechanical loading procedure was applied as a controlled stress induction mechanism to promote particle fragmentation rather than to determine conventional geotechnical parameters. A uniformly prepared quartz sand containing no additional mineral phases was used to ensure material consistency. Post-test specimens were examined through systematic visual and image-based analysis. The sample obtained from the 900 kPa test, where breakage was most pronounced, was analyzed in detail to characterize quartz fracture behavior under compressive and shear stress conditions using advanced image processing techniques. A deep learning-based mineral segmentation framework was developed using a ResNet50 architecture with transfer learning. A custom dataset consisting of high-resolution mineral images and corresponding pixel-level segmentation masks was constructed. The proposed model achieved 86.21% overall accuracy, a Dice coefficient of 91.35%, and an Intersection-over-Union (IoU) score of 84.07%. Validation results demonstrated strong generalization capability, with validation accuracy, Dice score, and IoU of 87.47%, 90.07%, and 81.96%, respectively. The high-precision segmentation performance enabled a comprehensive fracture analysis of 3333 quartz mineral images obtained from specimens exposed to systematic stress conditions. Full article

The rising environmental burden of Portland cement production has intensified the demand for eco-friendly binders that support sustainable construction. This study investigates the development and performance of eco-friendly self-compacting geopolymer concrete (SCGC) produced from industrial by-products, including fly ash (FA), ground granulated blast furnace slag (GGBFS), silica fume (SF), metakaolin (MK), and glass waste powder (GWP). Twenty-one binder formulations were evaluated for fresh-state workability, mechanical performance, durability, and microstructural characteristics under different curing regimes. Fresh properties were assessed using slump flow, V-funnel, L-box, and J-ring tests, while hardened-state evaluations included compressive and flexural strength, Young’s modulus, and water absorption. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis were performed on selected mixes to examine microstructural features and crystalline phase development. Results highlight a strong dependency of SCGC performance on binder composition and curing conditions. Mixes rich in GGBFS and SF demonstrated superior mechanical and durability performance, achieving compressive strengths of up to 102.4 MPa under water curing and 107.6 MPa under heat curing, along with negligible water absorption, reflecting a dense and well-developed gel matrix. SEM micrographs confirmed homogeneous, compact microstructures in high-performing mixes, while XRD analysis revealed broad amorphous humps indicative of well-formed N-A-S-H and C-A-S-H gel phases with minimal crystalline residues. In contrast, FA-dominant mixes displayed delayed strength development, and MK-GWP-rich systems exhibited higher porosity and reduced strength. This study underscores the significance of precursor synergy, optimized curing strategies, and microstructural refinement in tailoring SCGC for high-performance, durable, and low-carbon applications in sustainable construction with values ranged from 38.64 GPa (Mix 21) to 25.04 GPa (Mix 19) at 28 days. Stiffer mixes corresponded to denser matrices containing GGBFS and silica fume, whereas lower values were linked to weaker bonding and higher porosity. Full article